If given the opportunity, a terrorist organization such as al Qaeda could unleash Chemical, Biological, Radiological or Nuclear (CBRN) material in future attacks. Accordingly, future CBRN combat system requirements include an online network of traditional and non-traditional CBRN sensors to improve situation awareness and response to a CBRN event. The key to exploiting this advanced capability, however, will lie in the ability to combine and accurately interpret the disparate sensor detections so that a high-fidelity Single Integrate Picture (SIP) of the battlespace can be provided to the Combatant Commanders. To develop an effective SIP, the tracking and fusion algorithms must also overcome real-world challenges such as communication limitations, sensor registration problems, and real-time performance requirements. The DECISIVE ANALYTICS Corporation (DAC) team proposes to overcome these real-world problems through our innovative data fusion, sensor netting, and sensor resource management algorithms. The CBRN Real-time Advanced Classification and Tracking (CBRN-ReACT) system will improve threat identification and tracking accuracy by combining our state-of-the-art fusion framework, which naturally accommodates new and diverse sensor data as well as unanticipated sensor behavior in the face of increasingly difficult scenarios, with our tool for positioning sensors before and repositioning mobile sensors during a CBRN event.